import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from math import exp

#
#def loss_builder(loss_type):
#    
#    if loss_type == 'cross_entropy':
#        weight_1 = torch.Tensor([1,5,10,20])
#        criterion = nn.NLLLoss(weight=weight_1,ignore_index=255)
#        criterion_2 = DiceLoss()
#        criterion_3 = nn.BCELoss()
#        return 
#    elif loss_type == 'dice_loss':
#        weight_1 = torch.Tensor([1,5,10,20])
#        criterion_1 = nn.NLLLoss(weight=weight_1,ignore_index=255)
#        criterion_2 = EL_DiceLoss()
#        criterion_3 = nn.BCELoss()
#
#    if loss_type in ['mix_3','mix_33']:
#        criterion_1.cuda()
#        criterion_2.cuda()
#        criterion_3.cuda()
#        criterion = [criterion_1,criterion_2,criterion_3]
#
#    return criterion


class DiceLoss(nn.Module):
    def __init__(self, smooth=0.01):
        super(DiceLoss, self).__init__()
        self.smooth = smooth
        
    def forward(self, input, target):
        N = input.size(0)
        # input = torch.sigmoid(input)
        Dice = Variable(torch.Tensor([0]).float()).cuda()
        intersect = (input*target).sum()
        union = torch.sum(input) + torch.sum(target)
        Dice = (2*intersect+self.smooth)/(union+self.smooth)
        dice_loss = 1 - Dice
        return dice_loss/N


class Multi_DiceLoss(nn.Module):
    def __init__(self, class_num=5, smooth=0.001):
        super(Multi_DiceLoss, self).__init__()
        self.smooth = smooth
        self.class_num = class_num

    def forward(self, input, target):
        # input = torch.sigmoid(input)
        Dice = Variable(torch.Tensor([0]).float()).cuda()
        for i in range(0, self.class_num):
            input_i = input[:, i, :, :]
            target_i = (target == i).float()
            intersect = (input_i*target_i).sum()
            union = torch.sum(input_i) + torch.sum(target_i)
            dice = (2 * intersect + self.smooth) / (union + self.smooth)
            Dice += dice
        dice_loss = 1 - Dice/(self.class_num)
        return dice_loss


class EL_DiceLoss(nn.Module):
    def __init__(self, class_num=4, smooth=1, gamma=0.5):
        super(EL_DiceLoss, self).__init__()
        self.smooth = smooth
        self.class_num = class_num
        self.gamma = gamma

    def forward(self,input, target):
        input = torch.exp(input)
        self.smooth = 0.
        Dice = Variable(torch.Tensor([0]).float()).cuda()
        for i in range(1,self.class_num):
            input_i = input[:,i,:,:]
            target_i = (target == i).float()
            intersect = (input_i*target_i).sum()
            union = torch.sum(input_i) + torch.sum(target_i)
            if target_i.sum() == 0:
                dice = Variable(torch.Tensor([1]).float()).cuda()
            else:
                dice = (2 * intersect + self.smooth) / (union + self.smooth)
            Dice += (-torch.log(dice))**self.gamma
        dice_loss = Dice/(self.class_num - 1)
        return dice_loss


def _iou(pred, target, size_average=True):
    b = pred.shape[0]
    IoU = 0.0
    for i in range(0, b):
        # compute the IoU of the foreground
        Iand1 = torch.sum(target[i, :, :, :]*pred[i, :, :, :])
        Ior1 = torch.sum(target[i, :, :, :]) + torch.sum(pred[i, :, :, :])-Iand1
        IoU1 = Iand1/Ior1  # 交并比
        IoU = IoU + (1-IoU1)  # IoU loss is (1-IoU1) 损失
    # 除以N计算单个样本损失
    return IoU/b


class IOU(torch.nn.Module):
    def __init__(self, size_average=True):
        super(IOU, self).__init__()
        self.size_average = size_average  # 没用到，计算平均值，_iou直接计算了平均值

    def forward(self, pred, target):
        return _iou(pred, target, self.size_average)


# 计算IOU值
def IOU_loss(pred,label):
    iou_loss = IOU(size_average=True)
    iou_out = iou_loss(pred, label)
    print("iou_loss:", iou_out.data.cpu().numpy())
    return iou_out


def gaussian(window_size, sigma):
    gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
    return gauss/gauss.sum()


def create_window(window_size, channel=1):
    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
    window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
    return window


def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None):
    # Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh).
    if val_range is None:
        if torch.max(img1) > 128:
            max_val = 255
        else:
            max_val = 1

        if torch.min(img1) < -0.5:
            min_val = -1
        else:
            min_val = 0
        L = max_val - min_val
    else:
        L = val_range

    padd = 0
    (_, channel, height, width) = img1.size()
    if window is None:
        real_size = min(window_size, height, width)
        window = create_window(real_size, channel=channel).to(img1.device)

    mu1 = F.conv2d(img1, window, padding=padd, groups=channel)
    mu2 = F.conv2d(img2, window, padding=padd, groups=channel)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1 * mu2

    sigma1_sq = F.conv2d(img1 * img1, window, padding=padd, groups=channel) - mu1_sq
    sigma2_sq = F.conv2d(img2 * img2, window, padding=padd, groups=channel) - mu2_sq
    sigma12 = F.conv2d(img1 * img2, window, padding=padd, groups=channel) - mu1_mu2

    C1 = (0.01 * L) ** 2
    C2 = (0.03 * L) ** 2

    v1 = 2.0 * sigma12 + C2
    v2 = sigma1_sq + sigma2_sq + C2
    cs = torch.mean(v1 / v2)  # contrast sensitivity

    ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)

    if size_average:
        ret = ssim_map.mean()
    else:
        ret = ssim_map.mean(1).mean(1).mean(1)

    if full:
        return ret, cs
    return ret


def msssim(img1, img2, window_size=11, size_average=True, val_range=None, normalize=False):
    device = img1.device
    weights = torch.FloatTensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333]).to(device)
    # weights = torch.FloatTensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333])
    levels = weights.size()[0]
    mssim = []
    mcs = []
    for _ in range(levels):
        sim, cs = ssim(img1, img2, window_size=window_size, size_average=size_average, full=True, val_range=val_range)
        # print("sim",sim)
        mssim.append(sim)
        mcs.append(cs)

        img1 = F.avg_pool2d(img1, (2, 2))
        img2 = F.avg_pool2d(img2, (2, 2))

    mssim = torch.stack(mssim)
    mcs = torch.stack(mcs)

    # Normalize (to avoid NaNs during training unstable models, not compliant with original definition)
    if normalize:
        mssim = (mssim + 1) / 2
        mcs = (mcs + 1) / 2

    pow1 = mcs ** weights
    pow2 = mssim ** weights
    # From Matlab implementation https://ece.uwaterloo.ca/~z70wang/research/iwssim/
    output = torch.prod(pow1[:-1] * pow2[-1])
    return output


# Classes to re-use window
class SSIM(torch.nn.Module):
    def __init__(self, window_size=11, size_average=True, val_range=None):
        super(SSIM, self).__init__()
        self.window_size = window_size
        self.size_average = size_average
        self.val_range = val_range

        # Assume 1 channel for SSIM
        self.channel = 1
        self.window = create_window(window_size)

    def forward(self, img1, img2):
        (_, channel, _, _) = img1.size()

        if channel == self.channel and self.window.dtype == img1.dtype:
            window = self.window
        else:
            window = create_window(self.window_size, channel).to(img1.device).type(img1.dtype)
            self.window = window
            self.channel = channel

        return ssim(img1, img2, window=window, window_size=self.window_size, size_average=self.size_average)


class MSSSIM(torch.nn.Module):
    def __init__(self, window_size=11, size_average=True, channel=1):
        super(MSSSIM, self).__init__()
        self.window_size = window_size
        self.size_average = size_average
        self.channel = channel

    def forward(self, img1, img2):
        # TODO: store window between calls if possible,
        # return msssim(img1, img2, window_size=self.window_size, size_average=self.size_average)
        return msssim(img1, img2, window_size=self.window_size, size_average=self.size_average, normalize=True)

